Social information game system

ABSTRACT

A method to share challenges between users of a social game system is described. The method may include accessing relationship data reflecting a relationship between a first user and a second user, and accessing challenge data in a challenge database. Based on (1) the challenge data and (2) the relationship between the first user and the second user reflected by the relationship data, the challenge data is selectively communicated as part of a challenge about the first user to the second user, with the challenge forming part of a challenge game.

CLAIM OF PRIORITY

This patent application claims the benefit of priority, under 35 U.S.C.§119(e), to U.S. Provisional Patent Application Ser. No. 61/413,863,entitled “QUIZ GAMING SYSTEM,” filed on Nov. 15, 2010, which is herebyincorporated by reference herein in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright 2010, CAFEBOTS, INC. All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to computer game systems andmethods and more particularly, but not by way of limitation, to methodsand systems to present game mechanics to users of a game platform, andto process responses to the presented game mechanics.

BACKGROUND

The number of social media platforms with which users interact hasproliferated over the past few years. Examples of such social mediaplatforms include social networking systems (e.g., Facebook),professional networking systems (e.g., LinkedIn), virtual worldplatforms (e.g., Second Life), messaging systems (e.g., Google email(Gmail), Google Wave, Skype), blogging systems (e.g., Blogspot.com), andreview/rating systems (e.g., Yelp.com, Digg.com). Social networkingplatforms, such as Facebook, are continuing to gain popularity asplatforms on which users interact, communicate and share using multipletypes of data and communication channels. For example, a number ofsocial networking platforms provide one or more messaging tools, as wellas photo and video sharing capabilities. Virtual worlds similarly hostvibrant communities of people who interact, play, do business and evenfind romance online.

One type of application that is a popular on social networking systemsis the so called “challenge games,” which enabled users to communicatechallenges to each other regarding a variety of topics.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 is a block diagram illustrating an environment within which asocial game system may be deployed, according to some exampleembodiments;

FIG. 2 is a block diagram providing an architectural overview of asocial game system in the example form of a friend quiz system,according to some example embodiments;

FIG. 3 is a block diagram illustrating content of a friend quiz systemsocial database, according to some example embodiments;

FIG. 4A is a block diagram illustrating components of friend quiz systemalgorithms, according to some example embodiments;

FIG. 4B is a block diagram illustrating components of communicationsalgorithms, according to some example embodiments, that may form part ofthe friend quiz system algorithms;

FIG. 5 is a block diagram depicting details regarding game templates,according to some example embodiments;

FIG. 6 is a block diagram illustrating details regarding displays (e.g.,user interfaces and incorporated information) of a friend quiz system,according to some example embodiments;

FIG. 7 is a flowchart illustrating a method to share quiz questionsbetween users of a social game system, according to some exampleembodiments;

FIG. 8. is a user interface diagram showing an example game board userinterface, according to some example embodiments;

FIGS. 9-14 are a user interface diagrams illustrating various examplequestion user interfaces that may be presented by displays of a friendquiz system, according to some example embodiments; and

FIG. 15 is a block diagram of a machine in the example form of acomputer system within which instructions for causing the machine toperform any one or more of the methodologies discussed herein may beexecuted.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of some example embodiments. It will be evident, however,to one skilled in the art that the present invention may be practicedwithout these specific details.

According to one example embodiment, there is provided a system to sharegame challenges (e.g., quizzes) between users of a social game system.The system comprises at least on database storing relationship datarelating to relationships between the users of the social game systemand challenge data relating to a user challenge presentable within thecontext of a challenge game. A game module (e.g., a quiz game system),implemented using at least one processor, is configured to selectivelycommunicating the challenge data as part of a challenge relating to afirst user to a second user. The challenge forms part of a challengegame (e.g., a quiz game) and the challenge data is selectivelycommunicated based on at least one of the challenge data or therelationships between the users reflected by the relationship data.

In some example embodiment of, the challenge data includes a pluralityof challenge templates, and the game module is configured to select achallenge template from the plurality of challenge templates based onrelationship between the first user and the second user.

The at least one database may store at least one attribute of thechallenge data and at least one attribute of the relationship data, andthe game module may be configured to selectively communicate thechallenge data based on a comparison of the at least one attribute ofthe challenge data and the at least one attribute of the relationshipdata. The at least one attribute of the relationship data may a typeattribute, and the game module may be configured to selectivelycommunicate the challenge data based on a type of the relationshipbetween the first user and the second user as reflected in therelationship data. The at least one attribute of the relationship datamay further be a relationship strength attribute; and the game module isconfigured to selectively communicate the challenge data based on astrength of the relationship between the first user and the second useras reflected in the relationship data.

In some example embodiment, the system further includes an interfacecomponent configured to retrieve the relationship data from a thirdparty social networking platform.

The game module may be configured to generate the relationship databased on at least one of interactions within the social game system anddata received from a third party social networking platform. The gamemodule may further be configured to generate the relationship data usingchallenge processing algorithms, the challenge processing algorithmsincluding at least one of machine learning algorithms and non-machinelearning algorithms.

The relationship data includes a plurality of relationship attributespertaining to relationships between users of the social game system. Inone example, the plurality of relationship attributes include aknowledge attribute having a knowledge value indicative of an assessedknowledge of the second user with respect to the first user.

The game module may be configured to present a plurality of challengesto the second user regarding the first user, and to generate theknowledge value being based on an accuracy of responses of the seconduser to the plurality of challenges regarding the first user. The gamemodule may further be configured to receive a response to a firstchallenge of the plurality of challenges from the second user, and toaccess profile of the first user within the at least one database toassess the accuracy of the response.

In some example embodiments, the game module is configured to accessprofile data in the at least one database, wherein the profile datapertaining to the users of the social game system, wherein the challengedata comprises a plurality of challenge templates; and wherein the gamemodule is configured to select a challenge template from the pluralityof challenge templates based on profile data of the second userretrieved from the at least one database. The profile data of the seconduser may include historical communications data identifying historicalcommunications of challenge data to the second user; and the game modulemay be configured to perform the selection of the challenge templateusing the historical communications data. The game module mayfurthermore be configured to populate the selected challenge templateusing the profile data from the at least one database, the profile datapertaining to the users of the social game system. The game module mayalso generate the profile data based on at least one of interactionswithin the social game system and data received from a third partysocial networking platform. The generating of the profile data is, insome example embodiments, performed using challenge processingalgorithms, the challenge processing algorithms including at least oneof machine learning algorithms and non-machine learning algorithms.

In some example embodiments, the game module may further be configuredto generate respective attribute values for a plurality of attributes ofthe challenge data, and to selectively communicate the challenge databased on at least one of the plurality of attributes of the challengedata. The plurality of attributes of the challenge data may include anentertainment attribute indicative of a historical entertainment measurepertaining to the challenge data, a monetization attribute indicative ofa historical monetization measure pertaining to the challenge data,and/or an information attribute indicative of a historical informationcontribution attributable to the challenge data.

FIG. 1 is a block diagram illustrating an environment 100 within whichan example embodiment of a social game system 102, in the example formof a quiz game system, may be deployed. The quiz game system 102 isdiscussed herein as an example of a challenge game system. As such, theterm ‘quiz’ is used herein to reflect but one example of challenge thatmay be presented to a user of the social game system. The social gamesystem 102 is shown to host multiple quiz applications 104 and alsoprovides a number of interfaces 105 (e.g., a web interface and anapplication program interface (API) to provide respective web interfacesto users and programmatic interfaces to remote systems) via which thesocial game system 102 is accessible to external systems.

The social game system 102 is coupled via a network 106 (e.g., theInternet) to multiple social communication systems, which may include,for example, social networking systems 108, virtual world systems 110,and messaging systems 112. Each of the various types of socialnetworking systems 108 is shown to include both a web interface togenerate webpage interfaces to users and APIs to enable programmaticaccess to the relevant system.

The social game system 102 is also communicatively coupled via thenetwork 106 to a social media explorer system 114. The social mediaexplorer system 114, in one example embodiment, extracts social data(e.g., relationship data) and profile data from the social networkingsystem 108 and stores the extracted data in an associated database 116.

The environment 100 further includes any number of user computer systems120 hosting applications, such as browser application 122, that enableaccess to the social game system 102 and the various socialcommunication systems, via the network 106. While the example usercomputer system 120 is illustrated to host the browser application 122,the user computer system 120 may furthermore host any other number ofdedicated applications that may access data and services of any of theother systems shown to be accessible to the user computer system 120 viathe network 106.

FIG. 1 also depicts that any number of mobile devices 130 (e.g., smartphones, Personal Digital Assistants (PDAs), tablet computers, etc.) may,via the network 106, also access the described systems. As is the casewith the user computer systems 120, a mobile device 130 may host anynumber of applications (e.g., generic browser applications to access webinterfaces or dedicated applications to programmatically access systemsvia APIs). For example, a mobile device hosted application 132 may, inone example embodiment, comprise a quiz game client application thataccesses the quiz applications 104 via the API interface of the socialgame system interfaces 105.

For the purposes of the present example, functionality may be describedas being performed primarily on the social game system 102, which actsas in capacity of a server. However, the location of computationalfunctionality is becoming increasingly fungible as the computing powerof client devices (e.g., user computer systems 120 and mobile devices130) continue to increase. Accordingly, while certain functions andoperations are described as being performed, for the purpose ofexplanation, on the server site, any number of these functions andoperations may be migrated to a client-site device and, in other exampleembodiments, performed by dedicated applications or scripts executing onsuch client-site devices.

FIG. 2 is a block diagram providing an architectural overview of asocial game system in the example form of a friend quiz system (FQS)202. The quiz system 202 is coupled to a social media explorer system214, which is shown to, at least partially, to reside in the ‘cloud’ andto be accessible via the network 206 by the quiz system 202. Asexplained above, a social media explorer system 214 may be a system thatextracts data (e.g., profile and relationship data) from socialnetworking systems external to the quiz system 202, and that providesthis extracted data to the quiz system 202. In some example embodiments,the social media explorer system 214 may reside externally and separatefrom the quiz system 202 and communicate with the quiz system 202 viathe network 206. In other example embodiments, the social media explorersystem 214 may be deployed as part of the quiz system 202.

Turning now specifically to the components or subsystems of the quizsystem 202, a number of quiz games 220 are supported by the quiz system202 and, as will be described in further detail below, may be deployedas sequential ‘mini-games’ or levels of a larger, overarching game. Thegames 220 may include various game logic and game mechanics, and arefurthermore supported by quiz system algorithms 222, which may provideinput to the games 220 as well as process output (e.g., game results)from the games 220.

Both the quiz system algorithms 222 and quiz system games 220 arecommunicatively coupled to have access to quiz system databases 224that, in addition to storing game results data, also store profile andrelationship data for users of the quiz system 202. The quiz systemdatabases 224 may be populated with game result data from the games 220,processed data from the quiz system algorithms 222 (e.g., datapertaining to actions and interactions taken by users within the contextof the games 220), and data (e.g., profile and relationship data)provided from the social media explorer system 214.

The quiz system 220 further includes a number of quiz system gamedisplays 226, in the example form of data and interfaces that may bepresented to users. These displays, as will be described in furtherdetail below, may include data displays pertaining to the quiz system220 as a whole (e.g., progress through another data related to thecollection of games 220) and displays that are presented within thecontext of a particular quiz game 220, as well as the display ofexternal data (e.g., data provided by the social media explorer system214).

FIG. 3 is a block diagram illustrating further detail regarding contentof a friend quiz system social database 324, according to an exampleembodiment. The social database 324 may, in one example embodiment, beone of the databases that form part of the quiz system databases 224.

The social database 324 is accessible, via a network 306, to socialnetworking systems 308 and external preprocessed machine learningsystems 330. Data from both the social media systems 308 (e.g., asextracted by a social media explorer system 214 of FIG. 2) and theexternal preprocessed machine learning systems 330 may be used topopulate the social databases 324 via application interfaces 340 andmachine learning algorithms 342.

The external preprocessed machine learning systems 330 may include animputation engine 332, a connection type module 334 and a connectionstrength module 336. Briefly, the imputation engine 332 operates toimpute both profile and relationship data from social data (e.g.,profile, relationship and action data) retrieved from the socialnetworking systems 308. The connection type module 334 may operate toassess a connection type between users identified in social data (e.g.,to assess whether a relationship is a business or personalrelationship). The connection strength module 336 may assess and assigna value to a relationship strength attribute for a relationship betweentwo users identified in social data from social networking systems 308.To this end, a connection strength module 336 may examine social graphdata, reflecting a social graph, as extracted from one or more socialnetworking systems 308, and generate a strength attribute value based onproximity between users as reflected in the social graph data. To thisend, a strength value for a particular relationship between first andsecond users would be provided with a higher strength value for a closerproximity (e.g., a first level connection) as opposed to a distantproximity (e.g., a third level connection).

In addition to receiving data from external sources, such as socialnetworking systems 308, the social databases 324 may also be accessed(e.g., by read or write operations) by a number of the components of thequiz system 202 as described above with reference to FIG. 2. To thisend, FIG. 3 shows the quiz system algorithms 322, quiz system games 320and quiz system displays 326 communicatively coupled to the socialdatabase 324. It will be appreciated that information may be bothwritten into the databases and extracted from the databases using or viathe algorithms 322, games 320 and displays 326. The internal componentsmay also access specific data within the social databases 324 via theapplication interfaces 340 and/or the machine learning algorithms 342.

The data within the social databases 324 may be broadly classified asbeing either explicit data (e.g., data that is directly known as aresult of an explicit input by a user) or implicit data (e.g., data thatmay be inferred or imputed from other data or actions of one or moreusers). Furthermore, the data in the social database 324 may also bebroadly classified as being profile data (e.g., data pertaining toparticular attributes of a user, such as contact, demographic data), andnetwork data (e.g., data relating to relationships and connections toother users or entities).

FIG. 3 illustrates the social database 324 to include explicit profiledata 344 that may include, for example, objective data 346 (e.g.,profile data such as age, gender and other demographic data) andsubjective data 348 (e.g., interest in books, politics, movies etc.).The social database 324 also includes explicit network data 350 such as,for example, contacts 352 on social networking systems 308 and variousmedia connections 354 (e.g., other users or entities to which aparticular user has sent messages, such as Facebook messages, textmessages, etc.). The explicit profile data 344 and explicit network data350 may, in some embodiments, be provided directly into the socialdatabase 324 via application interfaces 340, from the social networkingsystems 308 (e.g., via a social media explorer system), or via thealgorithms 322, games 320 and displays 326.

The social database 324 is also shown to include implicit profile data360, which is profile data that may not have been directly stated, butwhich is implied, inferred, suggested or understood from other data. Forexample, the implicit profile data 360 may include both objective data362 not explicitly entered into social network data received fromexternal/internal sources, as well as subjective data 364 similarly notexplicitly provided via internal or external sources. The implicitprofile data 360 may also include objective data 366 that would not bedirectly enterable internally or externally, and subjective data 368,similarly not enterable via external or internal resources. Such‘unenterable’ data may, for example, be profile data that would nottypically be solicited from a user. Examples of objective data 362 notentered via a social networking system may include, for example, auser's location of birth or direct family connections. Subjective data368 that cannot be entered via social network may include, merely forexample, complex family connections (e.g., a father-in-law, if a spouseis not currently a member of the social network), or all places ofresidence of a user's lifetime.

The social database 324 may further include implicit network data 370,which may record any number of attributes regarding network (e.g.,relationship) data between users and/or entities. To this end, thenetwork data may comprise many-to-many mappings to record relationshipsand may attach values to various attributes of those relationships. Inother example embodiments, the implicit network data 370 may bereflected as a graph.

Attributes that may be associated with network or relationship data mayinclude type attributes (e.g., personal/business, close friend, family,school friend, work colleague, etc.) 372 and strength attributes 374(e.g., ‘close’ friend as opposed to a ‘regular’ friend, ‘current’co-worker as opposed to a ‘past’ co-worker).

As shown in FIG. 3, both the implicit profile data 360 and the implicitnetwork data 370 may be generated by machine learning algorithms 342.The machine learning algorithms 342 may operate to impute informationthat a user or entity has not explicitly provided to an externalresource (e.g., social network system 308) or internally (e.g., in thecontext of a game 320). The machine learning algorithms 342, based onexplicit profile data 344 and explicit network data 350, as well asinformation received from the games of the games 320, may impute orextract data to generate the implicit profile data 360 and implicitnetwork data 370.

It is further worth noting that each of the various types of profile andnetwork data described above with reference to FIG. 3 may constituteattributes that allow for the construction of multi-dimensional profileand network data descriptions pertaining to a particular user.Furthermore, these profile and network attributes may have binary values(e.g., a YES/NO) or measured or range values (e.g., a range valueexpressing the strength of a relationship on a scale of 1 to 10).

FIG. 4A is a block diagram illustrating components of friend quiz systemalgorithms 422, according to some example embodiments. At a high level,the quiz system algorithms 422 access both profile and network (e.g.,relationship) data within the social database 424, as well as gameresults data 421, reflecting profile, relation and interaction dataextracted from game play on the games 420. Based on these inputs, thequiz system algorithms 422 seek to optimize on quiz questions that maybe presented to a particular user regarding a further user, entity ortopic. Such questions may be presented directly by a game system to a“challenged user”, or may be presented to a “challenging user” forselective communication to a “challenged user”.

The quiz system algorithms 422 may optimize which users, or contacts, ina particular user's social network should be asked about in the contextof one or more of the quiz games 420. The quiz system algorithms 422 mayalso seek to optimize what one user should ask other users (e.g.,contacts) within the context of a quiz game 420. To this end, the quizsystem algorithms 422 may seek to predict the types of connections aparticular user shares with any other given contact and ‘a distance’metric between a user and a contact (e.g., which may be used as anapproximation of a strength of their relationship or connection). Withthis predicted data, the quiz system algorithms 422 may tailor quizgames 420, and in particular questions that are communicated from oneuser to other users (or to one user regarding another user), to onlyquery certain types of connections (or certain strengths of connections)for that user. For example, in the example of a ‘dare’ quiz game 420,questions within such an application may be optimized so that the useris only prompted or encouraged to challenge ‘close friends,’ rather thanco-workers on account of the nature and type of questions that may beincluded within a ‘dare’ quiz game.

Regarding the optimization of which topics or subject matter to be askedabout in questions of a particular quiz game 420, the quiz systemalgorithms 422, in one example embodiment, may predict the likelihoodthat each contact of a particular user will respond to an interaction(e.g., a question) using the category of information presented. Withthis predicted data, the quiz system algorithms 422 may maximize the useof questions within the context of games 420, by only asking questionsregarding topics likely to be answered by a particular contact of auser. For example, in the context of a ‘friend quiz’ game 420, such agame may be tailored to question a particular contact more frequentlyconcerning music and film (as opposed to books and hobbies), if thatparticular contact has shown in past behavior that they would prefer toanswer that category of question, or if their profile data (eitherexplicit 444 or implicit 460) reflects as such.

FIG. 4A shows the quiz system algorithms 422 to include multiplechallenge processing algorithms 464, which may be broadly categorized asmachine learning optimization algorithms 466 and non-machine learningalgorithms 468. The challenge processing algorithms 464, in one exampleembodiment, access challenge data (in the example form of quiz data)from a challenge database (in the example form of a quiz database). Thequiz database may take the form of library 461 of quiz templates. Aparticular template within the library 461 of quiz templates (e.g., quiz462) may address a particular topic or enquire about a particular typeof data or event. In one example embodiment, the quiz templates arepopulated by the challenge processing algorithms 464 with informationreceived either from the social databases 424, or in the context of aparticular game play, with a game result data 421. For example, aparticular quiz 462 may ask a first user about a favorite player on thesecond user's favorite football team. In this example, the quiz template462 may, at least partially, be populated with profile data (eitherexplicit 444 or implicit 460) regarding the second user's favoritefootball team.

As noted above, the challenge processing algorithms 422 seek to optimizethe selection and communication of quizzes (e.g., questions) to aparticular user regarding one or more further users (e.g., contacts ofthe first user). In one example embodiment, this selective communicationof questions is based on attributes of a quiz template 462. Theattributes of the quiz template 462 may include content of the quiztemplate 462, as well as other various attributes. Such attributes mayinclude topic, appropriateness for a particular type of relationship,appropriateness for a particular strength of relationship, and so forth.Thus, in selection and population of the quiz templates 462, thechallenge processing algorithms 464 may draw on social data extractedfrom social databases 424 (e.g., profile or relationship data), as wellas game result data 421 (e.g., real time or historic interactions,responses, etc.) within the context of a particular game 420.

The challenge processing algorithms 464 may, in one example embodiment,also generate both explicit 444/implicit 460 profile and network 450data, based on game result data 421 or extracted within the context ofgame play for a particular game 420, for inclusion within the socialdatabases 424.

Two examples of implicit network data 450 that may be generated by thechallenge processing algorithms 464 include a knowledge depth value, fora knowledge depth attribute, that is associated with a relationshipbetween two users. Specifically, the knowledge depth value may begenerated based on a determined assessment of the knowledge of aparticular user regarding a further user on a particular topic. Forexample, user 1 may have a deep knowledge of user 2 on a particulartopic (e.g., sports preferences). Accordingly, a knowledge depthattribute associated with the relationship between user 1 and user 2,specific to a ‘sport’ topic, may be assigned a relatively high attributevalue. This attribute value may be determined by the challengeprocessing algorithms 464, based on game results 421.

Another example of an attribute which may be associated with arelationship between first and second users is a knowledge breadth valuefor a knowledge breadth attribute. A knowledge breadth attribute may beindicative of the breadth of knowledge user 1 has with respect to user2, based on an assessment of user 1 regarding user 2 across multipletopics. For example, user 1 may, in the course of game play of games420, exhibit a knowledge regarding user 2 across a broad range oftopics. In this case, user 1 may be assigned a relatively high knowledgebreadth value for a knowledge breadth attribute related to user 2.

In performing a selection of quiz templates 462 for population andselective communication to a particular user or users, the challengeprocessing algorithms 464 may apply one or more goal specifications 470on which to optimize. In one embodiment, the goal specifications470 maybe defined as policies, which examine attributes of the quiz data (e.g.,quiz templates 462). For example, each of the quiz templates 462 beassigned an entertainment attribute 472, an information attribute 474and a monetization attribute 476, upon interaction with a user. Combinedwith prior knowledge of the user's activity and information, a level ofentertainment, information, and monetization may be predicted for anyselection among the quiz algorithms challenge processing. For example,game results may indicate that a particular quiz 462 exhibits a lowentertainment value, as a result of a low ‘dwell time’ with respect tothe particular quiz, but may exhibit a high information value, in thatdeep and rich information was provided by users in response to theparticular quiz. Similarly, for another quiz, a monetization attributevalue may be measured based on historical click throughs on informationassociated with the quiz to monetization opportunities. To this end, thechallenge processing algorithms 464 may be optimized to select quiztemplates 462 with high entertainment and information attribute valuesat one particular time for presentation within the context of games 420,but at another time select quiz questions that have a high monetizationattribute value, based on historical data.

As an additional example, machine learning optimization algorithms 466may predict that a certain contact of a user would enjoy playing acertain quiz game more than other contacts. To reach this prediction,the algorithms 466 used may include: the pre-processed information thatthis contact is strongly associated as a “personal friend;” the sharednumber of connections between this contact and the user on this socialplatform; and the contact's high response rate and quick response timewhen previously presented with quiz questions from this user. All ofthese inputs, including preprocessed data, profile data, and activitydata, respectively, may be included in the algorithms 466 to predict theentertainment gain of selecting this user. More specifically, onealgorithm that could be used would be a support vector machine. Usingthese inputs, combined with all the analogous inputs and response ratesof other users who previously were presented with quizzes, the supportvector regression can be trained to predict a likely response rate inthis scenario, and is also capable of learning more accurate predictionsas it iteratively creates predictions and is given feedback on itspredictions' accuracy.

FIG. 4B is a block diagram illustrating components of communicationsalgorithms 480, according to some example embodiments, that may alsoform part of the quiz system algorithms 422. The communicationsalgorithms 480 may operate to enhance virality of the social game systemthrough communications relating to game activity (e.g., postings to the“walls” of users). In performing a selection of communication to aparticular user or users, the communication algorithms 480 may apply oneor more goal specifications 481 on which to optimize. In one embodiment,the goal specifications may be defined as policies, which examineattributes of the communication data (e.g., communication templates482). Each communication may return a series of entertainmentattributes, information attributes and monetization attributes uponinteraction with a user. Combined with prior knowledge of the user'sactivity and information, a level of entertainment, information, andmonetization may be predicted for any selection among the communicationalgorithms.

For example, in the embodiment of a Facebook quiz game, a user's quizresults may be posted on contact's ‘wall.’ In this example, theeffectiveness of the ‘wall post,’ can be determined by the number ofresponses to the post, and the amount of time before the contact alsojoins the quiz game. These results can be used as inputs into thecommunication algorithms 480 to predict the likely success of wall postsin other instances.

As an additional example, it may be determined that users of a givendemographic are a more valuable target audience for advertisements orsales connected to a Facebook game. These users can be identified bytheir explicit listings of age and gender in their Facebook data, or, ifnot specifically reported, can be inferred in machine learningalgorithms 342. Using these demographics, a quiz game may be created tosuggest sending ‘application invites’ to these users more frequentlythan to users outside the demographic.

FIG. 5 is a block diagram depicting further details regarding gametemplates 560, according to an example embodiment. Specifically, asingle quiz 562 is shown to be comprised of a collection 564 ofquestions 565, with each question 565 having a number of attributesincluding, for example, a quiz type attribute 566, a quiz topicattribute 568 (which may assume one or more quiz topic attribute values)and a quiz target attribute 570 (which may likewise assume one or morequiz target attribute values). Example values for quiz type attributes566 may include friends' interests, memory of friends' posts, subjectivequestions by one or more friends, etc. Quiz topic attributes 568 may, inone example, be classified as either being objective 572 or subjective574. Examples of objective quiz topic attributes include status messagehistory, interests, friend connections, photos, game interactions,social data, etc. Examples of subjective quiz topic attributes includetrust in friends, friend probability to like a topic or activity, friendpolitical affiliations, etc. Quiz target attribute values may be addedfor one or more friends of a particular user that are previous or futurepotential targets for the relevant question.

FIG. 6 is a block diagram illustrating further details regardingdisplays 600 (e.g., user interfaces and incorporated information),according to some example embodiments, of a friend quiz system.

Various interfaces, examples of which are discussed in further detailbelow, include display data 602. The display data 602 may be compiledusing user results 604 and contact results 606 from a game historydatabase 608, which stores historical game results 621. Specifically,the display data 602 may include a leader-board ranking 610, a totalprogress display 612 and other data 614.

Dealing specifically with the leaderboard ranking 610, a ‘leader board’score value may be created for each user, and stored as part of theirprofile data within the databases 624. The leader board score value maybe based on a cumulative knowledge depth value, gleaned from knowledgedepth attributes for a particular user across relationships of that userwith other users. A cumulative knowledge breadth value, for knowledgebreadth attributes for relationships of the user across other users, maysimilarly be factored into a leader board score value for that user.Finally, a user's completion of games of the quiz system games 420 mayalso be factored into calculating a leader board score value.

A ranking within the leaderboard ranking 610 may be calculated in anumber of ways, such as, for example, with respect to all users of aquiz system, with respect to users that have played a particular game,or with respect to defined groups of ‘competitors’ against which a userhas chosen to compete. Such groups of competitors may, in some exampleembodiments, comprise friends or contacts of the user who are also usersof the quiz system. A ranking for a particular user within theleaderboard ranking 610 may be displayed alongside the rankings of agroup of ‘competitors’ for the relevant user.

Total progress display data 612 may take the form of a ‘game board,’ anexample of which is presented in FIG. 8. In one example embodiment,various quiz games 420 may constitute ‘mini-games’ in one overarchinggame, which may be referred to as a ‘game board,’ The ‘game board’display, as an example of a total progress display data 612, may be thehome screen of a quiz system, and a user may be brought to this homepageevery time they access the quiz system. The ‘game board’ display placesa user on a virtual board game where their progress through a sequenceof mini-games is visually displayed.

The displays 600 further include in-application displays 624, which mayin turn include leaderboard displays 622 that display rankinginformation with respect to a particular game (e.g., based on generalcriteria discussed above or with respect to specific game criteria) andresponse data 620 in the form of so-called ‘social mirror’ information.In one example embodiment, the response data 620 may provide feedback toa particular user regarding responses that they have previously providedwith respect to attributes (e.g., profile or networking) of other users.For example, if a particular user correctly predicts a contact or friendto have an interest in the San Francisco Giants, the response data 620,upon a subsequent determination that the friend or contact does in factlike the San Francisco Giants, may present a message to the contact tocommunicate that the user knew that the friend liked the San FranciscoGiants.

The response data 620 may, in example embodiments, also combine a user'srecent activity within the context of a quiz system with activities oftheir friends or contacts, to generate and dynamically display suchinformation back to a user.

Finally, the display 600 may include external data 626, which isexternal to a particular game or quiz system. Examples of such externaldata may include Twitter feeds, social media messages (e.g., Facebookmessages), text messages, and email messages.

FIG. 7 is a flowchart illustrating a method 700, according to oneexample embodiment, to share quiz questions between users of a socialgame system. The operations of the method 700 may be performed via thevarious components, subsystems and modules described above, but may, inalternative embodiments, also be distributed and performed by othermodules and components.

The method 700 commences at operation 702 with an access of social databy quiz algorithms 422. The social data may be retrieved from sourcesboth internal and external to a quiz system. In one example embodiment,the social data may comprise the explicit and implicit profile datastored within a social database 424, explicit and implicit network datastored within a social database 424, and a game result data 421, forexample stored within a game history database 608.

At operation 704, the quiz algorithms 422 access quiz applicationinteraction data (e.g., real time game results 421 or historical gameresults 621 stored within a game history database 608).

At operation 706, the challenge processing algorithms 464 may accessquiz data, in the example form of a library 461 of quiz templates 462.

At operation 708, the challenge processing algorithms 464 evaluate bothcontent and meta-data (e.g., attributes) of the various quiz templates462 within the library 461. This evaluation may include determining aquiz type, topic and targets for a particular quiz template 462.

At operation 710, the challenge processing algorithms 464 evaluate thesocial and interaction data accessed at operation 702 and 704 againstthe content and meta-data of the quiz templates.

At operation 712, the challenge processing algorithms 464 then output aselection of a particular quiz (e.g., as a collection of questions)based on the evaluation of the social and interaction data against thequiz data. Specifically, the quiz selection may include the selection ofone or more quiz templates for quizzes to be presented to a particularuser regarding a contact of that user, or may comprise a particular quizto be sent from one user to a contact or friend of that particular user.

At operation 714, the challenge processing algorithms 464 output arecipient list for the selected questions, and the selected questionsare thereafter communicated to the selected recipients at operation 716.

FIG. 8. is a user interface diagram showing an example game board userinterface 800. The interface depicts a series of mini-games (e.g., games420) in a sequence in which the games can be completed and also providesa visual indication of a particular user's progress through the sequenceof games.

FIGS. 9-14 are a user interface diagrams illustrating various examplequestion user interfaces 900-1400 that may be presented as part of thedisplays 600 discussed above with reference to FIG. 6.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A hardware module is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more hardware modules of a computer system (e.g., aprocessor or a group of processors) may be configured by software (e.g.,an application or application portion) as a hardware module thatoperates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarilyconfigured (e.g., programmed) to operate in a certain manner and/or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or processors or processor-implementedmodules. The performance of certain of the operations may be distributedamong the one or more processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processor or processors may be located in a singlelocation (e.g., within a home environment, an office environment or as aserver farm), while in other embodiments the processors may bedistributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., APIs).

Specific to “machine learning” algorithms, certain methods describedherein may be particularly processing-intensive, and may require asuperior degree of computing power. Machine learning algorithms arepartially distinguished by including certain techniques to analyze datathat scale in effectiveness with respect to the ability of theprocessor(s) used. That is, many problems approached in socialoptimization constitute “non-polynomial” problems (e.g., problems thatcannot be perfectly solved in a reasonable amount of time), and areinstead imperfectly solved using standard “heuristics” (e.g.,experience-based assumptions). Therefore, given the impossibility of aperfect solution, the margin of error in the heuristic used may bepartially determined by the capability of the computer. For example, onecommon machine learning technique, decision trees, often involvesreducing the complexity of the model when processing is limited (e.g.,“cost of complexity pruning”).

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, a data processing apparatus, e.g., a programmableprocessor, a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a FPGA or an ASIC.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 15 is a block diagram of a machine in the example form of acomputer system 1500 within which instructions for causing the machineto perform any one or more of the methodologies discussed herein may beexecuted. In alternative embodiments, the machine operates as astandalone device or may be connected (e.g., networked) to othermachines. In a networked deployment, the machine may operate in thecapacity of a server or a client machine in server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The example computer system 1500 includes a processor 1502 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 1504 and a static memory 1506, which communicatewith each other via a bus 1508. The computer system 1500 may furtherinclude a video display unit 1510 (e.g., a liquid crystal display (LCD)or a cathode ray tube (CRT)). The computer system 1500 also includes analphanumeric input device 1512 (e.g., a keyboard), a user interface (UI)navigation device 1514 (e.g., a mouse), a disk drive unit 1516, a signalgeneration device 1518 (e.g., a speaker) and a network interface device1520.

Machine-Readable Medium

The disk drive unit 1516 includes a machine-readable medium 1522 onwhich is stored one or more sets of instructions 1524 and datastructures (e.g., software) embodying or utilized by any one or more ofthe methodologies or functions described herein. The instructions 1524may also reside, completely or at least partially, within the mainmemory 1504 and/or within the processor 1502 during execution thereof bythe computer system 1500, with the main memory 1504 and the processor1502 also constituting machine-readable media.

While the machine-readable medium 1522 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase and/or associated caches and servers) that store the one ormore instructions or data structures. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machineand that causes the machine to perform any one or more of themethodologies of the present invention, or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., Erasable Programmable Read-Only Memory (EPROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 1524 may further be transmitted or received over acommunications network 1526 using a transmission medium. Theinstructions 1524 may be transmitted using the network interface device1520 and any one of a number of well-known transfer protocols (e.g.,HTTP). Examples of communication networks include a local area network(LAN), a wide area network (WAN), the Internet, mobile telephonenetworks, Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., WiFi and WiMax networks). The term “transmission medium”shall be taken to include any intangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machine,and includes digital or analog communications signals or otherintangible media to facilitate communication of such software.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

1. A method to share game challenges between users of a social gamesystem, the method comprising: accessing relationship data reflectingrelationships between the users of the social game system; accessingchallenge data in a challenge database; and based on at least one of thechallenge data or the relationships between the users reflected by therelationship data, selectively communicating the challenge data as partof a challenge relating to a first user to a second user, the challengeforming part of a challenge game.
 2. The method of claim 1, wherein thechallenge relates a first plurality of users that include first user. 3.The method of claim 1, wherein the challenge data includes a pluralityof challenge templates, and wherein the selective communication of thechallenge data comprises selecting a challenge template from theplurality of challenge templates based on relationship between the firstuser and the second user.
 4. The method of claim 1, includingdetermining at least one attribute of the challenge data, determining atleast one attribute of the relationship data; and selectivelycommunicating the challenge data based on a comparison of the at leastone attribute of the challenge data and the at least one attribute ofthe relationship data.
 5. The method of claim 3, wherein the at leastone attribute of the relationship data is a type attribute; and theselective communication is based on a type of the relationship betweenthe first user and the second user as reflected in the relationshipdata.
 6. The method of claim 4, including assessing the type of therelationship based on information communicated between the first and thesecond users.
 7. The method of claim 3, wherein the at least oneattribute of the relationship data is a relationship strength attribute;and the selective communication is based on a strength of therelationship between the first user and the second user as reflected inthe relationship data.
 8. The method of claim 7, including assessing thestrength of the relationship between the first and the second usersbased on at least one of a threshold level of trust between the firstuser and the second user determined from the relationship data; and athreshold degree of influence of the first user on the second user (orvice versa) determined from the relationship data.
 9. The method ofclaim 7, wherein the relationship data includes social graph data andthe strength of the relationship is based on proximity of the first userto the second user as reflected within the social graph data.
 10. Themethod of claim 3, wherein the at least one attribute of therelationship is at least one of a binary value relationship attributeand a range value relationship attribute.
 11. The method of claim 1,including retrieving the relationship data from a third party socialnetworking platform.
 12. The method of claim 1, including generating therelationship data based on at least one of interactions within thesocial game system and data received from a third party socialnetworking platform.
 13. The method of claim 12, wherein the generatingof the relationship data includes capturing explicit relationship dataregarding at least one of the first user or the second user.
 14. Themethod of claim 12, wherein the generating of the relationship dataincludes generating implicit relationship data regarding at least one ofthe first user or the second user.
 15. The method of claim 12, whereinthe generating of the relationship data is performed using challengeprocessing algorithms, the challenge processing algorithms including atleast one of machine learning algorithms and non-machine learningalgorithms.
 16. The method of claim 12, wherein the generating of therelationship data includes maintaining a plurality of relationshipattributes pertaining to relationships between users of the social gamesystem.
 17. The method of claim 16, wherein the plurality ofrelationship attributes include a knowledge attribute having a knowledgevalue indicative of an assessed knowledge of the second user withrespect to the first user.
 18. The method of claim 17, wherein theknowledge value attribute includes a knowledge depth value for aknowledge depth attribute indicative of a knowledge depth of the seconduser with respect to the first user, the knowledge depth value beingassessed based on a determined assessment of the knowledge of the seconduser relating to the first user on a specific topic.
 19. The method ofclaim 17, wherein the knowledge value attribute includes a knowledgebreadth value for a knowledge breadth attribute indicative of aknowledge breadth of the second user with respect to the first user, theknowledge breadth value being assessed based on a determined assessmentof the knowledge of the particular user relating to the first useracross a plurality of topics.
 20. The method of claim 17, includingranking the users of the social game system according to cumulativeknowledge values; and displaying the ranking of the users.
 21. Themethod of claim 20, wherein the ranking of a particular user isdetermined with respect to a subgroup of users of the social gamesystem.
 22. The method of claim 17, including presenting a plurality ofchallenges to the second user regarding the first user; and generatingthe knowledge value being based on an accuracy of responses of thesecond user to the plurality of challenges regarding the first user. 23.The method of claim 22, including receiving a response to a firstchallenge of the plurality of challenges from the second user; andaccessing a profile of the first user within a profile database toassess the accuracy of the response.
 24. The method of claim 23,including determining that the profile of the first user does notinclude an attribute value corresponding to the response received to thefirst challenge from the second user, monitoring the profile of thefirst user to determine an update to the profile of the first user toinclude the attribute value corresponding to the response; andresponsive to determining an update, providing an indication to thesecond user regarding the accuracy of the response.
 25. The method ofclaim 22, including presenting a first challenge of the plurality ofchallenges to the second user regarding the first user, receiving afirst response to the first challenge from the second user, advising thefirst user that the second user has provided the first response; andpresenting the first user with an option to provide a second response,pertaining to the second user, to the first challenge.
 26. The method ofclaim 25, including selectively presenting the first response to thefirst user based on the first user providing the second response to thefirst challenge.
 27. The method of claim 1, including accessing profiledata in a profile database, the profile data pertaining to the users ofthe social game system, wherein the challenge data comprises a pluralityof challenge templates; and wherein the selective communication of thechallenge data comprises selecting a challenge template from theplurality of challenge templates based on profile data of the seconduser retrieved from the profile database.
 28. The method of claim 27,wherein the profile data of the second user includes historicalcommunications data identifying historical communications of challengedata to the second user; and the selection of the challenge template isperformed using the historical communications data.
 29. The method ofclaim 27, wherein the selective communication of the challenge datafurther comprises populating the selected challenge template usingprofile data from the profile database, the profile data pertaining tothe users of the social game system.
 30. The method of claim 1,including determining at least one attribute of the challenge data,accessing profile data in a profile database, determining at least oneattribute of the profile data of the second user, and selectivelycommunicating the challenge data based on a comparison of the at leastone attribute of the challenge data and the at least one attribute ofthe of the profile data of the second user.
 31. The method of claim 30,including generating the profile data based on at least one ofinteractions within the social game system and data received from athird party social networking platform.
 32. The method of claim 31,wherein the generating of the profile data includes generating explicitprofile data regarding at least one of the first or the second user. 33.The method of claim 31, wherein the generating of the profile dataincludes generating implicit profile data regarding at least one of thefirst with the second user.
 34. The method of claim 31, wherein thegenerating of the profile data is performed using challenge processingalgorithms, the challenge processing algorithms including at least oneof machine learning algorithms and non-machine learning algorithms. 35.The method of claim 1, including generating respective attribute valuesfor a plurality of attributes of the challenge data; and selectivelycommunicating the challenge data based on at least one of the pluralityof attributes of the challenge data.
 36. The method of claim 35, whereinthe plurality of attributes of the challenge data include anentertainment attribute indicative of a historical entertainment measurepertaining to the challenge data.
 37. The method of claim 35, whereinthe plurality of attributes of the challenge data include a monetizationattribute indicative of a historical monetization measure pertaining tothe challenge data.
 38. The method of claim 35, wherein the plurality ofattributes of the challenge data include an information attributeindicative of a historical information contribution attributable to thechallenge data.
 39. The method of claim 38 wherein the historicalinformation contribution comprises at least one of a historical profileinformation contribution and a historical relationship informationcontribution.
 40. The method of claim 1, wherein the social game systemfacilitates access to a plurality of challenge games, the method furtherincluding accessing a sequence of the plurality of games, generating auser interface depicting the sequence of the plurality of games, andproviding a visual indication of progress of a user through the sequenceof the plurality of games.
 41. The method of claim 1, wherein thechallenge data is quiz data.
 42. A system to share game challengesbetween users of a social game system, the system comprising: at leaston database storing relationship data relating to relationships betweenthe users of the social game system and challenge data relating to auser challenge presentable within the context of a challenge game; and agame module, implemented using at least one processor, configured toselectively communicating the challenge data as part of a challengerelating to a first user to a second user, wherein the challenge formspart of a challenge game and wherein the challenge data is selectivelycommunicated based on at least one of the challenge data or therelationships between the users reflected by the relationship data. 43.The system of claim 42, wherein the challenge data includes a pluralityof challenge templates, and wherein the game module is configured toselect a challenge template from the plurality of challenge templatesbased on relationship between the first user and the second user. 44.The system of claim 42, wherein the at least one database stores atleast one attribute of the challenge data and at least one attribute ofthe relationship data; and the game module is configured to selectivelycommunicate the challenge data based on a comparison of the at least oneattribute of the challenge data and the at least one attribute of therelationship data.
 45. The system of claim 44, wherein the at least oneattribute of the relationship data is a type attribute; and the gamemodule is configured to selectively communicate the challenge data basedon a type of the relationship between the first user and the second useras reflected in the relationship data.
 46. The system of claim 44,wherein the at least one attribute of the relationship data is arelationship strength attribute; and the game module is configured toselectively communicate the challenge data based on a strength of therelationship between the first user and the second user as reflected inthe relationship data.
 47. The system of claim 42, comprising aninterface component configured to retrieve the relationship data from athird party social networking platform.
 48. The system of claim 42,wherein the game module is configured to generate the relationship databased on at least one of interactions within the social game system anddata received from a third party social networking platform.
 49. Thesystem of claim 48, wherein the game module is configured to generatethe relationship data using challenge processing algorithms, thechallenge processing algorithms including at least one of machinelearning algorithms and non-machine learning algorithms.
 50. The systemof claim 48, wherein the relationship data includes a plurality ofrelationship attributes pertaining to relationships between users of thesocial game system.
 51. The system of claim 50, wherein the plurality ofrelationship attributes include a knowledge attribute having a knowledgevalue indicative of an assessed knowledge of the second user withrespect to the first user.
 52. The system of claim 51, wherein the gamemodule is configured to present a plurality of challenges to the seconduser regarding the first user; and to generate the knowledge value beingbased on an accuracy of responses of the second user to the plurality ofchallenges regarding the first user.
 53. The system of claim 52, whereinthe game module is configured to receive a response to a first challengeof the plurality of challenges from the second user; and to accessprofile of the first user within the at least one database to assess theaccuracy of the response.
 54. The system of claim 42, wherein the gamemodule is configured to access profile data in the at least onedatabase, wherein the profile data pertaining to the users of the socialgame system, wherein the challenge data comprises a plurality ofchallenge templates; and wherein the game module is configured to selecta challenge template from the plurality of challenge templates based onprofile data of the second user retrieved from the at least onedatabase.
 55. The system of claim 54, wherein the profile data of thesecond user includes historical communications data identifyinghistorical communications of challenge data to the second user; and thegame module is configured to perform the selection of the challengetemplate using the historical communications data.
 56. The system ofclaim 54, wherein the game module is configured to populate the selectedchallenge template using the profile data from the at least onedatabase, the profile data pertaining to the users of the social gamesystem.
 57. The system of claim 54, wherein the game module isconfigured to generate the profile data based on at least one ofinteractions within the social game system and data received from athird party social networking platform.
 58. The system of claim 54,wherein the generating of the profile data is performed using challengeprocessing algorithms, the challenge processing algorithms including atleast one of machine learning algorithms and non-machine learningalgorithms.
 59. The system of claim 42, wherein the game module isconfigured to generate respective attribute values for a plurality ofattributes of the challenge data; and to selectively communicating thechallenge data based on at least one of the plurality of attributes ofthe challenge data.
 60. The system of claim 59, wherein the plurality ofattributes of the challenge data include an entertainment attributeindicative of a historical entertainment measure pertaining to thechallenge data.
 61. The system of claim 59, wherein the plurality ofattributes of the challenge data include a monetization attributeindicative of a historical monetization measure pertaining to thechallenge data.
 62. The system of claim 59, wherein the plurality ofattributes of the challenge data include an information attributeindicative of a historical information contribution attributable to thechallenge data.